Okay Wild fans it’s that time of year. The time of year where we look at our roster, put on a brave face, and convince ourselves that this team can win a playoff series. Will it happen? Almost certainly not, but Marian Gaborik ain’t walking through that door any time soon, so we might as well reach deep into our hearts and minds and force ourselves to believe that we have enough talent up front to be a relevant NHL team in the 2018-2019 season. Is our top returning goal scorer turning 34 this month? Maybe he is, maybe he’s not. Age is just a number. Is the average age of our top two centers over 34? See above answer. Do we have almost exactly the same team we’ve had for the last six years? The team that has made it to the playoffs each of those last six years and won two playoff series? More or less, yes we do. None of this sounds good on paper, but as Minnesota Sports Fans it is our duty to ignore all signs of disaster and forge ahead with the most positive, if not delusional outlook we can manage. The general consensus amongst hockey pundits and fans is that the Wild will be one of, if not the, most boring team this year–and honestly, I don’t disagree. There is nothing about the Wild that screams “must watch.” Their best players aren’t exactly show stoppers. The closest things they have to gamebreakers are probably Zucker and Granlund and there are about 15 guys in the Central Division alone that I’d rather watch in a vacuum than those two. I don’t think anyone is paying top dollar to watch Koivu and Staal slow the game down (although Stubhub shows otherwise) and Suter might be the hardest top tier defensemen to watch in NHL history. It’s the same goddamn team they’ve trotted out for the last six years for christ’s sake. Sure those six years have all been playoff years but there is absolutely nothing about this team that should make anyone expect a legitimate playoff run this year. That being said, the Wild should be competitive in a very deep Central division. They don’t have the dynamic players that some of the top end teams have but they have a shit ton of depth at forward and defense and they’re experienced in terms of regular season success, so who the hell knows. They only thing I know for sure is that it’s going to be a long, long season, regardless of whether it ends after 82 games, or like 87 games… Continue reading A Non Model Based Minnesota Wild Season Preview
In this week’s My Model Monday, I explored aging curves in not just the NHL, but in other professional leagues and junior hockey leagues. First and foremost, what are aging curves? Aging curves are just what they sound like: curves that associate player performance and health over time. For a point of reference, despite his mighty accomplishments at an old age, Jaromir Jagr saw his point production dip from over a point per game at age 25 to about 0.3 PTS/G at age 45. Jaromir Jagr is an incredibly interesting case and likely outlier, as few players have played into their mid-forties. At any rate, one can imagine that many players experience similar increases and decreases in production with age; therefore, using many samples of players, one can construct a curve that resembles a mean of all players aging, or, as we like to say in the reinsurance industry, an industry exposure curve. Continue reading My Model Monday: Hockey Aging Curves
Below is results to our (preliminary) NHL Draft Model that uses prospects’ statistical production, physical measurements, and other variables to predict the likelihood that a players assume a specific NHL Role (i.e., First Line / Top Pair, Second / Third Line / 2nd pair defensemen, Fourth Line / Bottom pair defensemen, and Non-NHL player). The model is still being fine-tuned, hence the preliminary results, and an in-depth methodology article will come in the future. In addition to the aforementioned role probabilities, there is also a predicted NHL point per game that is derived from our Hockey Translation Factors.
If you didn’t know, there are a lot of people in the world—7.6 billion to be exact! There are also a lot of people that play hockey. As a result, there are a lot of hockey leagues in the world. Wow. Okay, moving on… The National Hockey League (NHL) is seen as the premier hockey league in the world, but players don’t start their hockey career in the NHL, and most never make it to the NHL. Some would argue that it is possible to have a successful and prosperous hockey career even if you never play in the NHL. In this article, I attempt to quantify the differences between these leagues; more specifically, translating individual player production from one league to the next. This would allow us to say things such as Tony Cameranesi registered 50 points in 50 games, or 1.0 point per game, in the NCAA, thus you would expect him to produce xx amount in the AHL, yy amount in the KHL, zz amount in the NHL, etc.
In the sport of hockey, we often value players that are more physical, especially those that additionally produce points. In this week’s My Model Monday, I explored the importance of hits on NHL hockey games. Continue reading My Model Monday: Understanding the Impact of Hits in the NHL
Below is a table of our 2017-18 NHL Playoff Simulation and Elo Ratings. Rankings are based on Probability of Winning the Conference Championships. Tune in to this post for updates to these figures throughout the rest of the 2017-18 NHL Playoffs.
With the NBA and NHL playoffs in full swing, it gives us a good chance to look at which teams over/underachieved during the regular season using Pythagorean Win Expectation, and, in turn, what teams could exceed expectations in the playoffs. Continue reading My Model Monday: NBA & NHL Pythagorean Wins
Below is our Model 284 consensus bracket for the 2017-18 NHL Playoffs as well as some Model Factoids. As you will see from our first round Predictions and playoff simulation results, we do not necessarily pick the model’s predicted winner for every single game, but use all available information (e.g. injuries, areas model might be lacking, etc.) to make the best prediction on each series.
In sports, people love to categorize players by their playing style. For example, in hockey, people distinguish defensemen as offensive or defensive, or the rare all-around defensemen. In this week’s installment of My Model Monday, I look to create mathematical groupings of NHL defensemen using 2017-2018 NHL data.